Biology subjects

Lucas, T. M.

Publications and source records attributed to Lucas, T. M..

6 recordsLinked to original sources

bioRxiv2024

Glycosite Mapping and in situ Mass Spectrometry Imaging of MUC2 Glycopeptides via On-slide Digestion with Mucinase StcE

Many cancers are characterized by altered mucin expression and glycosylation, although the mechanistic relationship between tumor glycosylation and disease progression is not well-defined. Herein, our goal was to map specific mucin glycoforms in diseased tissue, enabling correlation of the tumor glycan profile with malignant features. To this end, we developed a workflow implementing on-tissue digestion with mucinase StcE, followed by matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and liquid chromatography coupled to mass spectrometry (LC-MS). To optimize our workflow, we analyzed four different mucinous carcinomas derived from colon, esophageal, and salivary gland tissue. Using this technique, we deduced the spatial distribution of StcE-generated O-glycopeptides within mucinous tumors using MALDI-IMS. Subsequent LC-MS analyses revealed the identity of different species detected in imaging experiments, in addition to comprehensively characterizing the mucinome and proteome of each tissue. Our coupled MS approach unveiled a striking mucin 2 (MUC2) expression pattern in two colorectal mucinous adenocarcinomas, in which different glycoforms clearly stratified regions within the tumor. Notably, our LC-MS experiments obtained near-complete sequence coverage over the mucin domains of MUC2, enabling glycoproteomic mapping of this canonical mucin in unprecedented depth. MUC2 glycosylation was dominated by the T and Tn antigens, with surprisingly little sialylation detected. However, O-glycans containing mono- and di-O-acetylated sialic acid were detected in low abundance. Finally, we obtained spectral evidence for an endogenous O-acetylated GalNAc, an O-glycan structure not previously reported in the literature. Overall, this proof-of-concept work underscores the potential of this technique to generate new research avenues in oncology and beyond. Significance StatementAberrant mucin expression and glycosylation are hallmarks of cancer, but how these changes promote malignant processes are not well understood. Solid tumors are highly heterogeneous in their cellular and molecular composition, and many advanced spatial techniques have emerged in recent years to study the tumor microenvironment (TME) for better understanding disease progression. Spatially resolved glycoprotein analyses typically detect either the protein or glycan components, but not both. We developed a workflow using a dual mass spectrometry approach to map the location of intact glycopeptides in mucinous tumors, enabled by on-tissue digestion with the mucin-specific protease StcE. Future applications of this method on larger patient cohorts will enhance our understanding of glycans in malignancy, identify disease biomarkers, and define therapeutic targets.

cancer biology↗

bioRxiv2024

Quantification and site-specific analysis of co-occupied N- and O-glycopeptides

Protein glycosylation is a complex post-translational modification that is generally classified as N- or O-linked. Site-specific analysis of glycopeptides is accomplished with a variety of fragmentation methods, depending on the type of glycosylation being investigated and the instrumentation available. For instance, collisional dissociation methods are frequently used for N-glycoproteomic analysis with the assumption that one N-sequon exists per tryptic peptide. Alternatively, electron-based methods are indispensable for O-glycosite localization. However, the presence of simultaneously N- and O-glycosylated peptides could suggest the necessity of electron-based fragmentation methods for N-glycoproteomics, which is not commonly performed. Thus, we quantified the prevalence of N- and O-glycopeptides in mucins and other glycoproteins. A much higher frequency of co-occupancy within mucins was detected whereas only a negligible occurrence occurred within non-mucin glycoproteins. This was demonstrated from analyses of recombinant and/or purified proteins, as well as more complex samples. Where co-occupancy occurred, O-glycosites were frequently localized to the Ser/Thr within the N-sequon. Additionally, we found that O-glycans in close proximity to the occupied Asn were predominantly unelaborated core 1 structures, while those further away were more extended. Overall, we demonstrate electron-based methods are required for robust site-specific analysis of mucins, wherein co-occupancy is more prevalent. Conversely, collisional methods are generally sufficient for analyses of other types of glycoproteins. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/602348v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@b14908org.highwire.dtl.DTLVardef@131e812org.highwire.dtl.DTLVardef@17b07cborg.highwire.dtl.DTLVardef@3cb199_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Optimized mucin-selective enrichment strategy to probe the mucinome

Mucin-domain glycoproteins are densely O-glycosylated and play critical roles in a host of healthy and diseasedriven biological functions. Previously, we developed a mucin-selective enrichment strategy by employing a catalytically inactive mucinase (StcE) conjugated to solid support. While this method was effective, it suffered from low throughput and high sample requirements. Further, the elution step required boiling in SDS, thus necessitating an in-gel digest with trypsin. Here, we optimized our previous enrichment method to include elution conditions amenable to mucinase digestion and downstream analysis with mass spectrometry. This increased throughput and lowered sample input while maintaining mucin selectivity and enhancing glycopeptide signal. We then benchmarked this technique against different O-glycan binding moieties for their ability to enrich mucins from various cell lines and human serum. Overall, the new method outperformed our previous procedure and all other enrichment techniques tested. This allowed for effective isolation of more mucin-domain glycoproteins, resulting in a high number of O-glycopeptides, thus enhancing our ability to analyze the mucinome.

biochemistry↗

bioRxiv2023

False positive glycopeptide identification via in-FAIMS fragmentation

High-field asymmetric waveform ion mobility spectrometry (FAIMS) separates glycopeptides in the gas phase prior to mass spectrometry (MS) analysis, thus offering the potential to analyze glycopeptides without prior enrichment. Several studies have demonstrated the ability of FAIMS to enhance glycopeptide detection but have primarily focused on N-glycosylation. Here, we evaluated FAIMS for O-glycoprotein and mucin-domain glycoprotein analysis using samples of varying complexity. We demonstrated that FAIMS was useful in increasingly complex samples, as it allowed for the identification of more glycosylated species. However, during our analyses, we observed a phenomenon called "in FAIMS fragmentation" (IFF) akin to in source fragmentation but occurring during FAIMS separation. FAIMS experiments showed a 2-5-fold increase in spectral matches from IFF compared to control experiments. These results were also replicated in previously published data, indicating that this is likely a systemic occurrence when using FAIMS. Our study highlights that although there are potential benefits to using FAIMS separation, caution must be exercised in data analysis because of prevalent IFF, which may limit its applicability in the broader field of O-glycoproteomics. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/542648v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@d94300org.highwire.dtl.DTLVardef@1bdc6dforg.highwire.dtl.DTLVardef@4b8ea8org.highwire.dtl.DTLVardef@82beed_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2023

Glycoproteomic landscape and structural dynamics of TIM family immune checkpoints enabled by mucinase SmE

Mucin-domain glycoproteins are densely O-glycosylated and play critical roles in a host of biological functions. In particular, the T cell immunoglobulin and mucin-domain containing family of proteins (TIM-1, -3, -4) decorate immune cells and act as key checkpoint inhibitors in cancer. However, their dense O-glycosylation remains enigmatic both in terms of glycoproteomic landscape and structural dynamics, primarily due to the challenges associated with studying mucin domains. Here, we present a mucinase (SmE) and demonstrate its ability to selectively cleave along the mucin glycoprotein backbone, similar to others of its kind. Unlike other mucinases, though, SmE harbors the unique ability to cleave at residues bearing extremely complex glycans which enabled improved mass spectrometric analysis of several mucins, including the entire TIM family. With this information in-hand, we performed molecular dynamics (MD) simulations of TIM-3 and -4 to demonstrate how glycosylation affects structural features of these proteins. Overall, we present a powerful workflow to better understand the detailed molecular structures of the mucinome.

cancer biology↗

bioRxiv2021

Mucin-mimetic glycan arrays integrating machine learning for analyzing receptor pattern recognition by influenza A viruses

Influenza A viruses (IAVs) exploit host glycans in airway epithelial mucosa to gain entry and initiate infection. Rapid detection of changes in IAV specificity towards host glycan classes can provide early indication of virus transmissibility and infection potential. IAVs use hemagglutinins (HA) to bind sialic acids linked to larger glycan structures and a switch in HA specificity from 2,3-to 2,6-linked sialoglycans is considered a prerequisite for viral transmission from birds to humans. While the changes in HA structure associated with the evolution of binding phenotype have been mapped, the influence of glycan receptor presentation on IAV specificity remains obscured. Here, we describe a glycan array platform which uses synthetic mimetics of mucin glycoproteins to model how receptor presentation in the mucinous glycocalyx, including glycan type and valency of the glycoconjugates and their surface density, impact IAV binding. We found that H1N1 virus produced in embryonated chicken eggs, which recognizes both receptor types, exclusively engaged mucin-mimetics carrying 2,3-linked sialic acids in their soluble form. The virus was able utilize both receptor structures when the probes were immobilized on the array; however, increasing density in the mucin-mimetic brush diminished viral adhesion. Propagation in mammalian cells produced a change in receptor pattern recognition by the virus, without altering its HA affinity, toward improved binding of 2,6-sialylated mucin mimetics and reduced sensitivity to surface crowding of the probes. Application of a support vector machine (SVM) learning algorithm as part of the glycan array binding analysis efficiently characterized this shift in binding preference and may prove useful to study the evolution of viral responses to a new host.

microbiology↗